Advancements in Regenerative Medicine: A Multidisciplinary Review of Innovations and Collaborative Approaches - Alwizrah, Saeed Ibrahim Saeed; Al bahr, Abrar Abdullah; ALRUWAILI, JAZZAA OTAYBAN HURAN; bakkari, Rehab walan; AL AHMARI, ABDULRHMAN MUSHBAB OUDAH; Alshahrani, naser Ayed naser; alwadie, Alanood ali; ALSHARARI, SAMI MAHLI HAMED; ALRUWAILI, MOHAMMAD BATAH HADIJ; alanzi, Ahood obeed; Alonazi , Alanoud Monzel; ALmotiri, Ameinah Mosem; et al. (Journal of Ecohumanism, 2024)
Background: Advances in regenerative medicine have transformed the ability to repair and restore damaged tissues and organs. The incorporation of artificial intelligence (AI) into this interdisciplinary discipline improves the analysis and use of complicated datasets, allowing for advances in treatment procedures. Methods: This review synthesizes the existing research on the use of AI technologies in regenerative medicine, with an emphasis on drug discovery, disease modeling, and customized treatment. A thorough search was undertaken across many databases, including PubMed, Scopus, and Google Scholar, to find relevant papers published up to 2023. Results: The results show that AI technologies, including deep learning and machine learning algorithms, considerably increase drug development efficiency by evaluating massive volumes of molecular data to uncover possible therapeutic options. Furthermore, AI improves illness modeling by allowing the development of patient-specific models that account for unique genetic and environmental variables. Predictive modeling using AI provides insights into therapy reactions, allowing for customized medical methods adapted to particular patient requirements. Conclusion: The use of AI in regenerative medicine offers a great opportunity to advance treatment techniques, improve diagnostic accuracy, and improve patient outcomes. However, issues like as data quality, ethical concerns, and regulatory compliance must be solved before AI can fully fulfill its promise in this industry. Future studies should include longitudinal studies to assess AI's long-term influence on healthcare procedures and patient care.